A COMPARISON BETWEEN SYNTHETIC OVER- SAMPLING EQUILIBRIUM AND OBSERVED SUBSETS OF DATA FOR EPIDEMIC VECTOR CLASSIFICATION

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In this work we provide results of data mining and machine learning techniques, which form the basis of our prediction model for snail density classification in relation to the Schistosomiasis epidemic disease. All experiments to date are cognitive components in the development of our prediction model for the epidemic disease Schistosomiasis. This disease is detrimental to the health of the communities of affected areas as well as the crop and cattle life. If detected for early warning of the disease, the local communities can be better prepared to deal with any consequences of a breakout. This report gives an insight into the relationship between using a snapshot sample of environment data for epidemic disease vector classification, as opposed to the construction of an increased synthetic dataset.
LanguageEnglish
Title of host publicationUnknown Host Publication
Number of pages1
Publication statusPublished - 22 Jun 2015
EventDragon 3 symposium. ESA Communications. -
Duration: 22 Jun 2015 → …

Conference

ConferenceDragon 3 symposium. ESA Communications.
Period22/06/15 → …

Fingerprint

Schistosomiasis
Disease Vectors
Data Mining
Snails
Health
Datasets
Machine Learning

Keywords

  • Earth observation
  • Vector born disease
  • Epidemic vector classification

Cite this

@inproceedings{a4c1d356e1e045e1a1988948da2a738f,
title = "A COMPARISON BETWEEN SYNTHETIC OVER- SAMPLING EQUILIBRIUM AND OBSERVED SUBSETS OF DATA FOR EPIDEMIC VECTOR CLASSIFICATION",
abstract = "In this work we provide results of data mining and machine learning techniques, which form the basis of our prediction model for snail density classification in relation to the Schistosomiasis epidemic disease. All experiments to date are cognitive components in the development of our prediction model for the epidemic disease Schistosomiasis. This disease is detrimental to the health of the communities of affected areas as well as the crop and cattle life. If detected for early warning of the disease, the local communities can be better prepared to deal with any consequences of a breakout. This report gives an insight into the relationship between using a snapshot sample of environment data for epidemic disease vector classification, as opposed to the construction of an increased synthetic dataset.",
keywords = "Earth observation, Vector born disease, Epidemic vector classification",
author = "Terence Fusco and Yaxin Bi and Nugent, {Chris D} and Shingli Wu",
year = "2015",
month = "6",
day = "22",
language = "English",
booktitle = "Unknown Host Publication",

}

A COMPARISON BETWEEN SYNTHETIC OVER- SAMPLING EQUILIBRIUM AND OBSERVED SUBSETS OF DATA FOR EPIDEMIC VECTOR CLASSIFICATION. / Fusco, Terence; Bi, Yaxin; Nugent, Chris D; Wu, Shingli.

Unknown Host Publication. 2015.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

TY - GEN

T1 - A COMPARISON BETWEEN SYNTHETIC OVER- SAMPLING EQUILIBRIUM AND OBSERVED SUBSETS OF DATA FOR EPIDEMIC VECTOR CLASSIFICATION

AU - Fusco, Terence

AU - Bi, Yaxin

AU - Nugent, Chris D

AU - Wu, Shingli

PY - 2015/6/22

Y1 - 2015/6/22

N2 - In this work we provide results of data mining and machine learning techniques, which form the basis of our prediction model for snail density classification in relation to the Schistosomiasis epidemic disease. All experiments to date are cognitive components in the development of our prediction model for the epidemic disease Schistosomiasis. This disease is detrimental to the health of the communities of affected areas as well as the crop and cattle life. If detected for early warning of the disease, the local communities can be better prepared to deal with any consequences of a breakout. This report gives an insight into the relationship between using a snapshot sample of environment data for epidemic disease vector classification, as opposed to the construction of an increased synthetic dataset.

AB - In this work we provide results of data mining and machine learning techniques, which form the basis of our prediction model for snail density classification in relation to the Schistosomiasis epidemic disease. All experiments to date are cognitive components in the development of our prediction model for the epidemic disease Schistosomiasis. This disease is detrimental to the health of the communities of affected areas as well as the crop and cattle life. If detected for early warning of the disease, the local communities can be better prepared to deal with any consequences of a breakout. This report gives an insight into the relationship between using a snapshot sample of environment data for epidemic disease vector classification, as opposed to the construction of an increased synthetic dataset.

KW - Earth observation

KW - Vector born disease

KW - Epidemic vector classification

M3 - Conference contribution

BT - Unknown Host Publication

ER -